Attach packages

library(tidyverse)
library(janitor)
library(lubridate)
library(here)
library(paletteer)
library(tsibble)
library(fable)
library(fabletools)
library(feasts)
library(forecast)
library(sf)
library(tmap)
library(mapview)

Monthly US energy consumption data

us_renew <- read_csv(here("data", "renewables_cons_prod.csv")) %>% 
  clean_names()
renew_clean <- us_renew %>% 
  mutate(description = str_to_lower(description)) %>% 
  filter(str_detect(description, pattern = "consumption")) %>% 
  filter(!str_detect(description, pattern = "total"))

Convert yyyymm column to a date

renew_date <- renew_clean %>% 
  mutate(yr_mo_day = lubridate::parse_date_time(yyyymm, "ym")) %>% 
  mutate(month_sep = yearmonth(yr_mo_day)) %>% 
  mutate(value = as.numeric(value)) %>% 
  drop_na(month_sep, value)

# Make a version where month and reat are in separate columns
renew_parsed <- renew_date %>% 
  mutate(month = month(yr_mo_day, label = TRUE)) %>% # store month in abbreviation, FALSE : month number
  mutate(year = year(yr_mo_day))

Look at the data

renew_gg <- ggplot(data = renew_date, 
                   aes(x = month_sep, 
                       y = value, 
                       group = description)) +
  geom_line(aes(color = description)) +
  labs(x = "Time",
       y = "Consumption (Trillion Btu)",
       color = "Energy type") +
  theme_bw()
# update color with paletteer
renew_gg +
  scale_color_paletteer_d("palettetown::electrike")

Coerce renew_parsed to a tsibble

renew_ts <- as_tsibble(renew_parsed, key = description, index = month_sep)

Let’s look at out ts data in a couple different ways

renew_ts %>% autoplot(value) # know to group by the key, x uses index

renew_ts %>% gg_subseries(value)

renew_ts %>% gg_season(value)

# make season plot use ggplot

ggplot(data = renew_parsed, aes(x = month, y = value, group = year)) +
  geom_line(aes(color = year)) +
  facet_wrap(~ description,
             ncol = 1,
             scales = "free",
             strip.position = "right")

Just look at the hydroelectric energy consumption

hydro_ts <- renew_ts %>% 
  filter(description == "hydroelectric power consumption")

hydro_ts %>% autoplot(value)

hydro_ts %>% gg_subseries(value)

hydro_ts %>% gg_season(value)

quaterly average consumption for hydro?

hydro_quater <- hydro_ts %>% 
  index_by(year_qu = ~(yearquarter(.))) %>%  # based on the existing group exists
  summarize(avg_consumtion = mean(value),
            description = description)
dcmp <- hydro_ts %>% 
  model(STL(value ~ season(window = 5)))

components(dcmp) %>% autoplot()

residuals
hist(components(dcmp)$remainder)

ACF
hydro_ts %>% 
  ACF(value) %>% 
  autoplot()

# each lag is one month

DANGER DANGER

hydro_model <- hydro_ts %>% 
  model(
    ARIMA(value),
    ETS(value)
  ) %>% 
  fabletools::forecast(h = "4 years") # forecast for the next four years


hydro_model %>% autoplot(filter(hydro_ts, year(month_sep) > 2010))

Make a world map!

world <- read_sf(dsn = here("data", "TM_WORLD_BORDERS_SIMPL-0.3-1"),
                 layer = "TM_WORLD_BORDERS_SIMPL-0.3")

mapview(world)